How Do We Improve Data Governance In Our Data Models?

Data governance in data models is both the critical foundation for metadata development and the weak link in many data modeling environments. From a Data Modeling Manager’s point of view, the costly time that’s wasted due to inaccurate metadata should instead be focused on governing the metadata and ensuring it meets requirements to be approved and reused. When models are submitted for analysis or approval, it’s concerning when there are discrepancies in metadata naming and definitions (if they exist) as well as in model design properties. A review is critical, yet the time required to sort out each model for inconsistencies can often exceed the time or budget available, often allowing incorrect structures to exist and be replicated in future models.

This inefficient and potentially dangerous situation is made even more challenging when there are new hires within the modeling area who must be initiated into the company’s specific methods, vocabulary and subject area content.

The Problem AND Solution – They’re Both in the Models

The Solution:
Incorporating data governance measures as an integral part of the modeling effort is the key to solving both dangerous discrepancy issues as well as reducing the excessive time it takes to model as well as introduce new hires into the modeling environment. When governance processes are employed durint the modeling process, metadata will be verified and managed for change. When clear, accessible standards, processes and rules documentation is easily available, each resource is empowered – without the persistent need to occupy a senior resource’s schedule.

Mission Impossible: Data models represent the company’s metadata – and metadata is at the heart of our business’ purpose, integrity and profitability. Let’s bring the technical aspects of data governance into the modeling function as a requirement, while making the process as painless as possible.

Should you accept this Mission…
You and your loyal team will develop and/or buy naming standards and data management processes for use across the enterprise (Somebody has to do it!). And the processes must be as simple yet effective as possible. When common sense and a ‘minimalist’ approach is the underlying method for developing a modeling ‘infrastructure’, enforcement will not be an issue.

Data Governance in Models Doesn’t Just Happen –

The Requirements

The To Do List

Devise a strategy to ensure metadata consistency – without which the organization will continue to create and propagate metadata that lacks quality. Incorrect analysis leads to costly business operations, bad decisions and sometimes legal risk due to erroneously developed production metadata. The strategy needs to promote trusted, reusable objects to ensure reliability in the standardization of objects and reduction of analysis time.

Data governance can and must be integrated into the modeling process via standards and processes that guide data design - bringing clarity, consistency and discipline/structure to the enterprise modeling environment. No matter the organizational structure (potentially multiple departments producing multiple project data models), all models represent enterprise data – the inclusion of ‘enterprise’ standards unifies disparate structures over time and assists in the integration of metadata between systems.

Data governance promotes and ensures excellent data design and prevents the introduction of data defects at the model source. A model management infrastructure, or framework, is the means to deploying and maintaining a data governance/modeling program.

The infrastructure must be:

Easily accessible (intranet based)

Easy to navigate, understand and execute

Efficient – each critical process should ask only for the minimal amount of information necessary

Enforced – adherence to standards and processes is possible when they make sense, are well-written and are streamlined down to critical elements. Approved, reusable structures reduce analysis and development time – which benefits everyone – we reduce the cost of each project while ensuring the ability to integrate standardized metadata

Integrating Data Governance into the modeling process must be managed as a collection of tasks including reviews and approvals. The management is too complex to be done on an ad hoc basis. A ‘Modeling Infrastructure’ must be developed to provide a structure within which governance can thrive. The content in the infrastructure framework should be well-structured, well-written and client specific.

A strategy for component reusability – reducing the time for metadata analysis/development while gaining consistency for both Agile and Waterfall development

Easy maintenance process – Things change. Processes and standards may/will be modified or added – a metadata and model management infrastructure is a living environment. Change is inevitable… our documentation must be easily modified and published

Additionally:

The framework’s content must be written from an enterprise view – bringing corporate standardization to the modeling efforts of various departments and project teams

The use of a data modeling tool that has been (or will be) standardized across all modeling groups is required

Knowledge Availability becomes a reality when the infrastructure resides on the web – let’s empower the new hires ‘to get up to speed by themselves, and allow the mature resources to do their work without interruption

Assign the most qualified resources to each task – we’re all naturally better at some things. Use people’s strengths: if someone is detail-oriented, make them the Data Model Repository Administrator (in whatever tool) – people perform magnificently when doing what they like

Bad Data is No longer Acceptable

Data Governance is no longer optional – the inability to integrate metadata into various systems, measurable amounts of time and money lost on incorrect analysis and the very real consequences of bad data is no longer acceptable. Ultimately, the bottom line is affected negatively as we risk extensive rework, confusion and customers who are not being well served.

Most organizations have some standards for the ‘big’ objects: entities, attributes, tables & columns, and some may have good processes around the modeling requirements. But it’s rare to find a modeling environment that has sufficient standards, processes and a documented overall strategy for how metadata is created, modified and distributed.

Building a modeling framework is time consuming and requires the resources to have an ‘aerial’ view as to which processes are necessary, knowledge specifics on each aspect of the model and object development cycle and be able to write clearly with a unified voice. Reviews at key junctures, approvals, consistency and distribution are the basic elements of a well-devised model management strategy. If your organization has qualified resources and the time to allow them to build a comprehensive model management framework, then ‘Build’ might be the right direction for you.

But there is another option: ‘EM-SOS!’™ 2.0 (Enterprise Modeling – Set of Standards)
EM-SOS!™ 2.0 provides all the critical elements for a successful ‘governance-oriented’ modeling environment. The company’s existing standards and procedures may be integrated, or EM-SOS!™ 2.0 can be the robust source of new material – all material residing in 1 place which is accessible to all resources and a means of education for new hires.
EM-SOS™ 2.0! may be purchased in one of 3 tiers: Beginning (Jumpstart), Intermediate and Advanced to meet your current or growing model management level of maturity. EM-SOS!™ includes:

Talk To Us About Scheduling A Consultation

Not only do we sell data modeling software packages, but our extensive knowledge allows us to not only support your organization in proper use, but our own standards products are also available to ensure data efficiencies.

Knowledge & Reputation

Sandhill Consultants is a leader in the Enterprise Data Design & Management space. Our principle focus is ensuring that clients leverage their data to achieve optimum business outcomes.

Sandhill provides a best practices approach to Data Governance, Data Architecture, Business Information Analysis, and Data Validation for our clients to better understand and control their information.